factor_analysis: Factor Analysis with Varimax Rotation

In AFIT-R/anomalyDetection: Implementation of Augmented Network Log Anomaly Detection Procedures

Description

factor_analysis reduces the structure of the data by relating the correlation between variables to a set of factors, using the eigen-decomposition of the correlation matrix.

Usage

factor_analysis(data, hc_points)

Arguments

data	numeric data
hc_points	vector of eigenvalues [designed to use output from horns_curve]

Value

A list containing:

1. fa_loadings: numerical matrix with the original factor loadings

2. fa_scores: numerical matrix with the row scores for each factor

3. fa_loadings_rotated: numerical matrix with the varimax rotated factor loadings

4. fa_scores_rotated: numerical matrix with the row scores for each varimax rotated factor

5. num_factors: numeric vector identifying the number of factors

References

H. F. Kaiser, "The Application of Electronic Computers to Factor Analysis," Educational and Psychological Measurement, 1960.

See Also

horns_curve for computing the average eigenvalues used for hc_points argument

Examples

# Perform Factor Analysis with matrix \code{x}
x <- matrix(rnorm(200*3), ncol = 10)

x %>%
  horns_curve() %>%
  factor_analysis(x, hc_points = .)

AFIT-R/anomalyDetection documentation built on Oct. 14, 2019, 5:24 p.m.